Implantable and external devices are used to monitor physiologic signals of human and animal subjects. These devices may incorporate various types of sensors and can measure and record signals from those sensors for processing by a system or monitoring center located remote from the subject, or in other cases, the device may perform some or all of the desired signal processing and forward the resulting information to a remote system for display, recording, or further processing.
The signal processing is performed to extract information from the signal in order to assess the physiological condition of the monitored subject and often to evaluate the response of the subject to a therapy or experimental protocol. For example, devices that measure ECG and blood pressure are routinely used to assess cardiovascular function. Clinicians can use this information to make therapy decisions and researchers can use this information to assess the safety and utility of experimental therapies. This information is also used for closed loop control of therapy delivery. In other examples, measurements of peripheral nerve activity (PNA), respiration, blood oxygen, blood glucose, EEG, EMG, heart sounds and blood flow signals are processed to extract information for clinical or research purposes.
There is an increasing reliance on automatic processing to extract information in order to reduce labor and costs and to more consistently and accurately evaluate the condition of the subject. In therapeutic devices automatic extraction of this information is often essential for feedback control. However, accurate automatic extraction of information is often challenging or is compromised by the presence of noise.
In some physiologic signal processing applications, automated analysis is complicated by the fact that measured signals are the result of activity of multiple sources, referred to as multi-source signals. An example of a multi-source signal is ECG measured on the surface of the body where electrical activity is sensed from both the atria and ventricles as well as skeletal muscles. It is useful, for example, to observe atrial activity independent of ventricular activity in order to improve the detection of atrial arrhythmias. Current techniques for providing signal source extraction of multi-source signals, such as independent component analysis (ICA), assume independence of sources and performance is compromised when this assumption is invalid, such as is the case when separating atrial and ventricular activity in an ECG. In addition, ECGs are often recorded from ambulatory subjects using a small number of sensing leads, further complicating signal source extraction due to the mixing of sources inherent in a small number of leads.
Other signals, such as peripheral nerve activity (PNA) and brainstem auditory response, have proven difficult to analyze because of very low signal-to-noise ratio (SNR). Visual analysis of these signals is often inadequate to detect important features and obtain a quantitative evaluation.
Many physiological signal processing techniques have been difficult to successfully implement under certain conditions, particularly when processing signals from ambulatory subjects where the signals are often quite noisy. For example, measurements of ECG parameters such as heart rate, QT interval, PR interval, as well as systolic and diastolic blood pressure may contain errors as a result of the presence of noise. Detection of ventricular and atrial arrhythmias in ECG may have excessive incidence of false positives due to the inability of a signal processing algorithm to provide accurate detection, particularly in the presence of noise. Likewise, the presence of noise may result in inaccurate and inconsistent evaluation of cardiac pathologies that are reflected in ECG morphology. Because of lack of confidence in the accuracy of results, human review has often been used to confirm results or correct errors made by automated analysis algorithms.
Inaccuracies in performance can also result in excess telecom costs when monitoring ambulatory subjects. For example, some types of ambulatory ECG monitoring devices employ on-board signal processing to detect arrhythmias and forward the detected arrhythmias to a monitoring center where they are further processed and reviewed by a human being using a data review system. Because of limitations in existing algorithms, there is a high rate of false positive arrhythmia detections in the ambulatory device that results in a high volume of data transmitted from the patient to a monitoring center. This results in excessive telecommunications expense, the need for additional memory in the ambulatory monitoring device, and additional expense to manually review the data received at the monitoring center.
Various methods of data compression are also limited in their ability to provide high levels of compression with minimal signal distortion in part due to the presence of noise. More efficient data compression can reduce the volume of data that must be stored in memory on an ambulatory monitoring device as well as reduce the volume of data transmitted from the monitored subject. In certain applications, this can result in a reduction in telecom expense and a reduction in power consumption in the ambulatory monitoring device, leading to a reduction in the device size and extension of battery life.
The presence of noise in physiological signals can be a limiting factor in providing accurate and consistent computerized evaluations and extraction of information, but the removal of noise has been complicated by the fact that the noise often has spectral content that falls within the bandwidth of the signals of interest (referred to as in-band noise). For example atrial signals can be contaminated by electrical activity of the ventricle, and ECG signals can be contaminated by EMG from the skeletal muscles. The plethora of signal sources contained within a limited number of channels measured in a surface ECG, with each channel containing mixed interdependent signals, renders problematic the independent observation of the sources of these multisource signals. This problem is further complicated when signals are acquired from closely spaced electrodes and are contaminated by noise, as is usually the case when monitoring patients outside a clinic or hospital. This characterization is not only common to electrocardiogram (ECG) signals acquired with surface or subcutaneous leads but is also common to electrograms (EGM) measured with intracardiac leads, blood pressure signals, pulse oximetry signals, peripheral nerve activity (PNA) recordings, signals representing non-invasive measurements of intracranial pressure, and other physiologic signals collected from ambulatory subjects. Current filtering techniques such as bandpass filtering are effective in removing noise without distorting the signal when the spectral content of the noise and signal are separated in the frequency domain. Many filtering techniques capable of removing in-band noise, such as independent component analysis require that noise and signal content are uncorrelated and independent, an inaccurate assumption for most physiological signals.
Removing at least some of the in-band noise, or denoising, of physiological signals can be useful in the improvement of accuracy of computerized evaluations and has been an objective of many prior efforts. However, the success of many prior efforts has been limited. Various techniques have also been limited in their ability to report characteristics of information derived by an algorithm relative to noise, artifact, or signal morphology changes. These and other matters have presented challenges to the design and implementation of devices, systems and methods for processing physiological signals.